Instructions to use microsoft/focalnet-tiny-lrf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/focalnet-tiny-lrf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/focalnet-tiny-lrf") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny-lrf") model = AutoModelForImageClassification.from_pretrained("microsoft/focalnet-tiny-lrf", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/focalnet-tiny-lrf: direct link, hf CLI and curl.
- Browser
- Download file 115 MB
-
https://huggingface.co/microsoft/focalnet-tiny-lrf/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/focalnet-tiny-lrf/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/focalnet-tiny-lrf/resolve/main/pytorch_model.bin
115 MB
- Xet hash:
- 0baa2d20706649198c6de195803cd41d9570539fa303096c92f2c4961ddd19fe
- Size of remote file:
- 115 MB
- SHA256:
- e4e68629dac6e041e2ffbd0aa793f216cf18598871dfe480b14e5c3f26ba32b9
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